""" 群体间交互管理器 管理不同群体之间的交互行为 """ from panda3d.core import Vec3 import random from typing import List, Dict class InteractionManager: """ 群体间交互管理器 负责处理不同群体之间的交互行为，如竞争、合作、捕食等 """ def __init__(self, config): self.config = config self.interactions = [] # 存储群体间交互关系 self.interaction_history = [] # 交互历史记录 def add_interaction(self, swarm1: Dict, swarm2: Dict, interaction_type: str, strength: float = 1.0): """ 添加群体间交互关系 :param swarm1: 第一个群体 :param swarm2: 第二个群体 :param interaction_type: 交互类型 ('cooperative', 'competitive', 'predator_prey', 'neutral') :param strength: 交互强度 (0.0-1.0) """ interaction = { 'swarm1': swarm1, 'swarm2': swarm2, 'type': interaction_type, 'strength': strength, 'active': True } self.interactions.append(interaction) def remove_interaction(self, interaction): """ 移除群体间交互关系 """ if interaction in self.interactions: self.interactions.remove(interaction) def update_interactions(self, swarms: List[Dict]): """ 更新所有群体间交互 :param swarms: 所有群体列表 """ for interaction in self.interactions: if interaction['active']: self._process_interaction(interaction, swarms) def _process_interaction(self, interaction: Dict, swarms: List[Dict]): """ 处理单个群体间交互 :param interaction: 交互关系 :param swarms: 所有群体列表 """ swarm1 = interaction['swarm1'] swarm2 = interaction['swarm2'] interaction_type = interaction['type'] strength = interaction['strength'] if interaction_type == 'cooperative': self._process_cooperative_interaction(swarm1, swarm2, strength) elif interaction_type == 'competitive': self._process_competitive_interaction(swarm1, swarm2, strength) elif interaction_type == 'predator_prey': self._process_predator_prey_interaction(swarm1, swarm2, strength) elif interaction_type == 'neutral': # 中性交互，不产生影响 pass def _process_cooperative_interaction(self, swarm1: Dict, swarm2: Dict, strength: float): """ 处理合作交互 群体之间相互帮助，共享资源或信息 :param swarm1: 第一个群体 :param swarm2: 第二个群体 :param strength: 交互强度 """ # 合作行为：群体成员倾向于向另一个群体的中心移动 if swarm1.members and swarm2.members: # 计算群体中心 center1 = self._calculate_swarm_center(swarm1.members) center2 = self._calculate_swarm_center(swarm2.members) # 对两个群体的成员施加合作力 for member in swarm1.members: # 向第二个群体中心移动的趋势 coop_force = (center2 - member['position']).normalized() * strength * 0.5 member['cooperative_force'] = coop_force for member in swarm2.members: # 向第一个群体中心移动的趋势 coop_force = (center1 - member['position']).normalized() * strength * 0.5 member['cooperative_force'] = coop_force # 记录交互历史 self._record_interaction_history(swarm1, swarm2, 'cooperative', strength) def _process_competitive_interaction(self, swarm1: Dict, swarm2: Dict, strength: float): """ 处理竞争交互 群体之间争夺资源或领域 :param swarm1: 第一个群体 :param swarm2: 第二个群体 :param strength: 交互强度 """ # 竞争行为：群体成员倾向于远离另一个群体 if swarm1.members and swarm2.members: # 计算群体中心 center1 = self._calculate_swarm_center(swarm1.members) center2 = self._calculate_swarm_center(swarm2.members) # 对两个群体的成员施加竞争排斥力 for member in swarm1.members: # 远离第二个群体中心 competition_force = (member['position'] - center2).normalized() * strength * 0.7 member['competition_force'] = competition_force for member in swarm2.members: # 远离第一个群体中心 competition_force = (member['position'] - center1).normalized() * strength * 0.7 member['competition_force'] = competition_force # 记录交互历史 self._record_interaction_history(swarm1, swarm2, 'competitive', strength) def _process_predator_prey_interaction(self, predator_swarm: Dict, prey_swarm: Dict, strength: float): """ 处理捕食者-猎物交互 一个群体捕食另一个群体 :param predator_swarm: 捕食者群体 :param prey_swarm: 猎物群体 :param strength: 交互强度 """ if predator_swarm.members and prey_swarm.members: # 捕食者行为：寻找最近的猎物 for predator in predator_swarm.members: nearest_prey = self._find_nearest_member(predator, prey_swarm.members) if nearest_prey: # 向猎物移动 predation_force = (nearest_prey['position'] - predator['position']).normalized() * strength predator['predation_force'] = predation_force # 猎物行为：逃离最近的捕食者 for prey in prey_swarm.members: nearest_predator = self._find_nearest_member(prey, predator_swarm.members) if nearest_predator: # 远离捕食者 escape_force = (prey['position'] - nearest_predator['position']).normalized() * strength * 1.2 prey['escape_force'] = escape_force # 记录交互历史 self._record_interaction_history(predator_swarm, prey_swarm, 'predator_prey', strength) def _calculate_swarm_center(self, members: List[Dict]) -> Vec3: """ 计算群体中心位置 :param members: 群体成员列表 :return: 群体中心位置 """ if not members: return Vec3(0, 0, 0) center = Vec3(0, 0, 0) for member in members: center += member['position'] center /= len(members) return center def _find_nearest_member(self, member: Dict, other_members: List[Dict]) -> Dict: """ 查找最近的成员 :param member: 参考成员 :param other_members: 其他成员列表 :return: 最近的成员 """ if not other_members: return None nearest = None min_distance = float('inf') for other in other_members: if other != member: # 排除自己 distance = (member['position'] - other['position']).length() if distance < min_distance: min_distance = distance nearest = other return nearest def _record_interaction_history(self, swarm1: Dict, swarm2: Dict, interaction_type: str, strength: float): """ 记录交互历史 :param swarm1: 第一个群体 :param swarm2: 第二个群体 :param interaction_type: 交互类型 :param strength: 交互强度 """ record = { 'swarm1_id': id(swarm1), 'swarm2_id': id(swarm2), 'type': interaction_type, 'strength': strength, 'timestamp': self._get_current_time() } self.interaction_history.append(record) # 限制历史记录数量 if len(self.interaction_history) > 1000: self.interaction_history = self.interaction_history[-1000:] def _get_current_time(self): """ 获取当前时间戳 """ import time return time.time() def get_interaction_stats(self) -> Dict: """ 获取交互统计信息 :return: 交互统计信息 """ stats = { 'total_interactions': len(self.interactions), 'active_interactions': len([i for i in self.interactions if i['active']]), 'cooperative': len([i for i in self.interactions if i['type'] == 'cooperative']), 'competitive': len([i for i in self.interactions if i['type'] == 'competitive']), 'predator_prey': len([i for i in self.interactions if i['type'] == 'predator_prey']), 'neutral': len([i for i in self.interactions if i['type'] == 'neutral']), 'history_count': len(self.interaction_history) } return stats def clear_interactions(self): """ 清空所有交互关系 """ self.interactions.clear() self.interaction_history.clear() def get_interactions_for_swarm(self, swarm: Dict) -> List[Dict]: """ 获取指定群体的所有交互关系 :param swarm: 群体对象 :return: 交互关系列表 """ return [i for i in self.interactions if i['swarm1'] == swarm or i['swarm2'] == swarm]